Students

STAT394 – Probability, Random Processes and Statistics for Engineers

2019 – S1 Day

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Convenor
Jun Ma
jun.ma@mq.edu.au
Contact via Email
12 Wally's Walk Office 5.26
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Lecturer
Ayse Bilgin
ayse.bilgin@mq.edu.au
Contact via Email
12 Wally's Walk Office 6.35
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Credit points Credit points
3
Prerequisites Prerequisites
6cp at 200 level including MATH235(P)
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description
This unit develops the probabilistic and statistical ideas needed to apply the theory of random processes to engineering fields such as signal processing and communications. Topics covered include probability, random variables, expectation, random processes, stationarity, ergodicity, spectral density, limit theorems, markov chains, estimation theory.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • Understand and compute probabilities, conditional probabilities, random variables.
  • Understand and work with probability density functions, expectations, moment generating functions.
  • Be able to compute the joint distributions and expectations of functions of more than one random variable.
  • Understand and use the concepts of hypothesis testing, probability of false alarm.
  • Be able to compute the likelihood and maximum likelihood estimators.

Assessment Tasks

Name Weighting Hurdle Due
Assignment 1 15% No 3rd April
Assignment 2 15% No 29th May
SGTA Participation 10% No Weeks 2 to 13
Final Examination 60% No 11 to 12 June, 2019

Assignment 1

Due: 3rd April
Weighting: 15%

Submit a hard copy to your lecturer by 1pm on the due date. There is no “group work” assessment in this unit. All work is to be the student’s own. All assignments must be submitted by the due date and time. No marks will be given for late submission unless an extension has been granted.
On successful completion you will be able to:
  • Understand and compute probabilities, conditional probabilities, random variables.
  • Understand and work with probability density functions, expectations, moment generating functions.

Assignment 2

Due: 29th May
Weighting: 15%

Submit a hard copy to your lecturer by 1pm on the due date. There is no “group work” assessment in this unit. All work is to be the student’s own. All assignments must be submitted by the due date and time. No marks will be given for late submission unless an extension has been granted.
On successful completion you will be able to:
  • Be able to compute the joint distributions and expectations of functions of more than one random variable.
  • Understand and use the concepts of hypothesis testing, probability of false alarm.
  • Be able to compute the likelihood and maximum likelihood estimators.

SGTA Participation

Due: Weeks 2 to 13
Weighting: 10%

Students will contribute to discussions in the class.
On successful completion you will be able to:
  • Understand and compute probabilities, conditional probabilities, random variables.
  • Understand and work with probability density functions, expectations, moment generating functions.
  • Be able to compute the joint distributions and expectations of functions of more than one random variable.
  • Understand and use the concepts of hypothesis testing, probability of false alarm.
  • Be able to compute the likelihood and maximum likelihood estimators.

Final Examination

Due: 11 to 12 June, 2019
Weighting: 60%

The final Examination will be held during the mid-year Examination period. The final Examination is 2 hours long (with an additional 10 minutes’ reading time). It will cover all topics in the unit. The final examination is closed book. Students may take into the final Exam TWO A4 pages of notes handwritten or typed (not photocopied) on BOTH sides. Calculators will be needed but must not be of the text/programmable type.

The only excuse for not sitting an examination at the designated time is because of documented illness or unavoidable disruption. In these special circumstances you may apply for special consideration via ask.mq.edu.au

If you receive special consideration for the final exam, a supplementary exam will be scheduled in the interval between the regular exam period and the start of the next session.  By making a special consideration application for the final exam you are declaring yourself available for a resit during the supplementary examination period and will not be eligible for a second special consideration approval based on pre-existing commitments.  Please ensure you are familiar with the policy prior to submitting an application. You can check the supplementary exam information page on FSE101 in iLearn (bit.ly/FSESupp) for dates, and approved applicants will receive an individual notification one week prior to the exam with the exact date and time of their supplementary examination.
On successful completion you will be able to:
  • Understand and compute probabilities, conditional probabilities, random variables.
  • Understand and work with probability density functions, expectations, moment generating functions.
  • Be able to compute the joint distributions and expectations of functions of more than one random variable.
  • Understand and use the concepts of hypothesis testing, probability of false alarm.
  • Be able to compute the likelihood and maximum likelihood estimators.

Delivery and Resources

There are four contact hours per week, comprised of three lectures and one STGA class. Check the timetable for the times and locations of classes.

Please consult iLearn or the Unit webpage for details of consultation hours.

Unit Schedule

This course covers the following six topics:

Topic 1 Probability, conditional probability, independence, mutually exclusive events
Topic 2 Random variables, distribution function, discrete and continuous random variables, expectation, moments, moment generating function
Topic 3 Bivariate cdf and pdf, independent rvs, transformations, sums of rvs, central limit theorem, conditional expectation, distributions derived from the normal
Topic 4 Unbiased estimation, likelihood function, maximum likelihood, Cramer-Rao lower bound, asymptotic behaviour
Topic 5 Simple and composite hypotheses, critical regions, Neyman-Pearson lemma, significance levels, power, false alarm rates
Topic 6 Inequalities, laws of large numbers, order statistics

 

Learning and Teaching Activities

Lecture

Three hours per week

STGA

One hour per week

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Undergraduate students seeking more policy resources can visit the Student Policy Gateway (https://students.mq.edu.au/support/study/student-policy-gateway). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

If you would like to see all the policies relevant to Learning and Teaching visit Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central).

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/study/getting-started/student-conduct​

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Learning Skills

Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to improve your marks and take control of your study.

Student Services and Support

Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.

Student Enquiries

For all student enquiries, visit Student Connect at ask.mq.edu.au

If you are a Global MBA student contact globalmba.support@mq.edu.au

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Graduate Capabilities

Discipline Specific Knowledge and Skills

Our graduates will take with them the intellectual development, depth and breadth of knowledge, scholarly understanding, and specific subject content in their chosen fields to make them competent and confident in their subject or profession. They will be able to demonstrate, where relevant, professional technical competence and meet professional standards. They will be able to articulate the structure of knowledge of their discipline, be able to adapt discipline-specific knowledge to novel situations, and be able to contribute from their discipline to inter-disciplinary solutions to problems.

This graduate capability is supported by:

Learning outcomes

  • Understand and compute probabilities, conditional probabilities, random variables.
  • Understand and work with probability density functions, expectations, moment generating functions.
  • Be able to compute the joint distributions and expectations of functions of more than one random variable.
  • Understand and use the concepts of hypothesis testing, probability of false alarm.
  • Be able to compute the likelihood and maximum likelihood estimators.

Assessment tasks

  • Assignment 1
  • Assignment 2
  • SGTA Participation
  • Final Examination

Learning and teaching activities

  • Three hours per week
  • One hour per week

Critical, Analytical and Integrative Thinking

We want our graduates to be capable of reasoning, questioning and analysing, and to integrate and synthesise learning and knowledge from a range of sources and environments; to be able to critique constraints, assumptions and limitations; to be able to think independently and systemically in relation to scholarly activity, in the workplace, and in the world. We want them to have a level of scientific and information technology literacy.

This graduate capability is supported by:

Learning outcomes

  • Understand and compute probabilities, conditional probabilities, random variables.
  • Understand and work with probability density functions, expectations, moment generating functions.
  • Be able to compute the joint distributions and expectations of functions of more than one random variable.
  • Understand and use the concepts of hypothesis testing, probability of false alarm.
  • Be able to compute the likelihood and maximum likelihood estimators.

Assessment tasks

  • Assignment 1
  • Assignment 2
  • SGTA Participation
  • Final Examination

Learning and teaching activities

  • Three hours per week
  • One hour per week

Problem Solving and Research Capability

Our graduates should be capable of researching; of analysing, and interpreting and assessing data and information in various forms; of drawing connections across fields of knowledge; and they should be able to relate their knowledge to complex situations at work or in the world, in order to diagnose and solve problems. We want them to have the confidence to take the initiative in doing so, within an awareness of their own limitations.

This graduate capability is supported by:

Learning outcomes

  • Understand and compute probabilities, conditional probabilities, random variables.
  • Understand and work with probability density functions, expectations, moment generating functions.
  • Be able to compute the joint distributions and expectations of functions of more than one random variable.
  • Understand and use the concepts of hypothesis testing, probability of false alarm.
  • Be able to compute the likelihood and maximum likelihood estimators.

Assessment tasks

  • Assignment 1
  • Assignment 2
  • SGTA Participation
  • Final Examination

Learning and teaching activities

  • Three hours per week
  • One hour per week

Effective Communication

We want to develop in our students the ability to communicate and convey their views in forms effective with different audiences. We want our graduates to take with them the capability to read, listen, question, gather and evaluate information resources in a variety of formats, assess, write clearly, speak effectively, and to use visual communication and communication technologies as appropriate.

This graduate capability is supported by:

Assessment task

  • SGTA Participation

Learning and teaching activity

  • Three hours per week
  • One hour per week

Changes from Previous Offering

There are only two assignments now.

Textbooks and other reference material

Students should access springerlink via the library website (type springer in the search bar and click on the online access button) and look for suitable texts to download (free). One such book, that has more content than we need, is

Probability with Applications in Engineering, Science, and Technology, by Carlton and Devore.

Another is

Introduction to Probability and Statistics for Engineers, by Milan Holický.

A textbook that has been used in the past is

Richard H. Williams, Probability, Statistics, and Random Processes for Engineers, Cengage Learning, 2003.

Other good references are

A. Leon-Garcia, Probability, Statistics, and Random Processes for Electrical Engineering, 3 ed. Upper Saddle River, New Jersey: Prentice-Hall, 2008

and

A. Papoulis, Probability, Random Variables, and Stochastic Processes. New York: McGraw-Hill, 2002.

There are many introductory mathematical statistics and/or random (stochastic) processes books, and many are suitable references.

The notes in iLearn will be fairly exhaustive, and will be put up approximately one week in advance of their delivery, or earlier.